This course offers a practical introduction to the fundamentals of data analysis and R
To acquire the statistical understanding to design an appropriate analysis and the practical skills to implement the analysis in R and present the results.
Data analysis is fundamental for arriving at scientific conclusions and testing different hypotheses. This course offers a hands-on introduction to statistical analyses including: exploratory data analysis, testing differences in populations, p-values, power calculations, multiple testing, confounding, linear regression, maximum likelihood, model selection, and logistic regression; along with the fundamentals of R programming including markdown and data handling with the tidyverse.
Lecture slides will be available
Prerequisites / Notice
Access to Rstudio with some markdown and tidyverse packages installed.
Performance assessment information (valid until the course unit is held again)
The performance assessment is only offered in the session after the course unit. Repetition only possible after re-enrolling.
Mode of examination
oral 20 minutes
Additional information on mode of examination
Final grade: 62.5% oral examination, 37.5% project work. Project work has to be re-done in case of repetition. The course includes compulsory continuous performance assessments in the form of project work/assignments, which constitute 37.5% of the final grade.
This information can be updated until the beginning of the semester; information on the examination timetable is binding.
No public learning materials available.
Only public learning materials are listed.
No information on groups available.
There are no additional restrictions for the registration.